sklearn.feature_selection.GenericUnivariateSelect¶
- class sklearn.feature_selection.GenericUnivariateSelect(score_func=<function f_classif at 0x2b80949fb578>, mode='percentile', param=1e-05)[source]¶
Univariate feature selector with configurable strategy.
Parameters: score_func : callable
Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues).
mode : {‘percentile’, ‘k_best’, ‘fpr’, ‘fdr’, ‘fwe’}
Feature selection mode.
param : float or int depending on the feature selection mode
Parameter of the corresponding mode.
Attributes: scores_ : array-like, shape=(n_features,)
Scores of features.
pvalues_ : array-like, shape=(n_features,)
p-values of feature scores.
See also
- f_classif
- ANOVA F-value between labe/feature for classification tasks.
- chi2
- Chi-squared stats of non-negative features for classification tasks.
- f_regression
- F-value between label/feature for regression tasks.
- SelectPercentile
- Select features based on percentile of the highest scores.
- SelectKBest
- Select features based on the k highest scores.
- SelectFpr
- Select features based on a false positive rate test.
- SelectFdr
- Select features based on an estimated false discovery rate.
- SelectFwe
- Select features based on family-wise error rate.
Methods
fit(X, y) Run score function on (X, y) and get the appropriate features. fit_transform(X[, y]) Fit to data, then transform it. get_params([deep]) Get parameters for this estimator. get_support([indices]) Get a mask, or integer index, of the features selected inverse_transform(X) Reverse the transformation operation set_params(**params) Set the parameters of this estimator. transform(X) Reduce X to the selected features. - __init__(score_func=<function f_classif at 0x2b80949fb578>, mode='percentile', param=1e-05)[source]¶
- fit(X, y)[source]¶
Run score function on (X, y) and get the appropriate features.
Parameters: X : array-like, shape = [n_samples, n_features]
The training input samples.
y : array-like, shape = [n_samples]
The target values (class labels in classification, real numbers in regression).
Returns: self : object
Returns self.
- fit_transform(X, y=None, **fit_params)[source]¶
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters: X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
Returns: X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
- get_params(deep=True)[source]¶
Get parameters for this estimator.
Parameters: deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
- get_support(indices=False)[source]¶
Get a mask, or integer index, of the features selected
Parameters: indices : boolean (default False)
If True, the return value will be an array of integers, rather than a boolean mask.
Returns: support : array
An index that selects the retained features from a feature vector. If indices is False, this is a boolean array of shape [# input features], in which an element is True iff its corresponding feature is selected for retention. If indices is True, this is an integer array of shape [# output features] whose values are indices into the input feature vector.
- inverse_transform(X)[source]¶
Reverse the transformation operation
Parameters: X : array of shape [n_samples, n_selected_features]
The input samples.
Returns: X_r : array of shape [n_samples, n_original_features]
X with columns of zeros inserted where features would have been removed by transform.
- set_params(**params)[source]¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns: self :